Informatics in Education

Scrum is a widely-used framework in industry, so many schools apply it to their software engineering courses, particularly capstone courses. Due to the differences between students and industrial professionals, changing Scrum is necessary to fit capstone projects. In this paper, we suggest a decision-making process to assist instructors in developing a strategy to adapt Scrum for their course. This framework considers critical differences, such as student’s workloads and course schedules, and keeps the Agile principles and Scrum events. To evaluate the adapted Scrum, we investigated student’s learning experiences, satisfaction, and performance by quantitatively analyzing user story points and source codes and qualitatively studying instructor’s evaluations, student’s feedback, and Sprint Retrospective notes. Our two case studies about adapted Scrum showed that having daily stand-up meetings in every class was not helpful, student’s satisfaction positively correlated to the difficulty of the task they tackled, and the project provided good learning experiences.

While virtual learning environments (VLEs) present several advantages, such as space-time flexibility, they are still not including proper opportunities and resources for students to engage in collaborative activities with their peers. Recent approaches, for example, are based on resources that are not standard for VLEs or usual for students. Thus, their integration with VLEs is not simple. This paper conducted a theoretical investigation to identify strategies that could induce collaborative behaviours in students. These strategies were implemented as learning objects running in a VLE and a quasi-experimental research design was conducted with 133 students. The results show that the approach promotes collaborative interactions between students and also tend to improve their learning outcomes. Moreover, learning objects use a conceptualization that is already established over the e-learning community, simplifying their integration with VLEs.

Although widely used, the SCORM metadata model for content aggregation is difficult to be used by educators, content developers and instructional designers. Particularly, the identification of contents related with each other, in large repositories, and their aggregation using metadata as defined in SCORM, has been demanding efforts of computer science researchers in pursuit of the automation of this process. Previous approaches have extended or altered the metadata defined by SCORM standard. In this paper, we present experimental results on our proposed methodology which employs ontologies, automatic annotation of metadata, information retrieval and text mining to recommend and aggregate related content, using the relation metadata category as defined by SCORM. We developed a computer system prototype which applies the proposed methodology on a sample of learning objects generating results to evaluate its efficacy. The results demonstrate that the proposed method is feasible and effective to produce the expected results.

Content personalization in educational systems is an increasing research area. Studies show that students tend to have better performances when the content is customized according to his/her preferences. One important aspect of students particularities is how they prefer to learn. In this context, students learning styles should be considered, due to the importance of this feature to the adaptivity process in such systems. Thus, this work presents an efficient approach for personalization of the teaching process based on learning styles. Our approach is based on an expert system that implements a set of rules which classifies learning objects according to their teaching style, and then automatically filters learning objects according to students' learning styles. The best adapted learning objects are ranked and recommended to the student. Preliminary experiments suggest promising results.

In this paper, we present a standard definition for learning objects, a controversy around it, and the resulted working definition, along with features to be held by learning objects, benefits of the object-oriented approach for learning, some pros and cons for using learning objects, and finally some quality standard guidelines for these objects. In addition, we introduce shortly a taxonomy of learning object types and the metadata standards that can be used for learning objects and the way they inter-relate. An overview of the content and capabilities of the instructional digital libraries available on the web is presented too. We conclude by pointing out some possible solutions for meaningful use of the learning objects that can be found on the web, either by construction of really useful community instructional digital libraries, or by using non-authoritative metadata to find these learning resources. Involving the conscious user in the process of making sense of the huge quantity of learning resources to be available on the web is, in our view, the only straightforward way to having fast access to the most appropriate (instructional) resource that is needed for a particular (educational) aim.